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Agglomeration detection in horizontal stirred bed reactor based on autoregression model by acoustic emission signals / Yefeng Zhou in Industrial & engineering chemistry research, Vol. 51 N° 36 (Septembre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 36 (Septembre 2012) . - pp. 11629-11635 Titre : Agglomeration detection in horizontal stirred bed reactor based on autoregression model by acoustic emission signals Type de document : texte imprimé Auteurs : Yefeng Zhou, Auteur ; Zhengliang Huang, Auteur ; Ren Congjing, Auteur Année de publication : 2012 Article en page(s) : pp. 11629-11635 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Acoustic  emission  Modeling  Stirred  tank  reactor  Agglomeration Résumé : Agglomeration occurring in horizontal stirred bed reactors (HSBR) for polyolefin production has negative impacts on the efficiency of the reactor operation and may sometimes lead to unscheduled shutdown of the plant. In this paper, an autoregression (AR) model based on acoustic emission (AE) technique has been proposed to establish the qualitative relationship between AE signals and agglomeration in the HSBR. In this method, the frequency of AE signal varies with particles of different sizes striking the reactor walls. From the cold model experiments, it was found that AR power spectrum became fluctuant after the addition of agglomerations into laboratorial scale HSBR, and meanwhile the low frequency band energy ratio and the variance of AE signals kept rising. Furthermore, this AE-based AR model was also successfully applied to detect the agglomeration in an industrial HSBR unit, showing that the method could monitor agglomerations in an environmentally friendly manner and with fairly good accuracy. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26350328 [article] Agglomeration detection in horizontal stirred bed reactor based on autoregression model by acoustic emission signals [texte imprimé] / Yefeng Zhou, Auteur ; Zhengliang Huang, Auteur ; Ren Congjing, Auteur . - 2012 . - pp. 11629-11635. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 36 (Septembre 2012) . - pp. 11629-11635 Mots-clés : Acoustic  emission  Modeling  Stirred  tank  reactor  Agglomeration Résumé : Agglomeration occurring in horizontal stirred bed reactors (HSBR) for polyolefin production has negative impacts on the efficiency of the reactor operation and may sometimes lead to unscheduled shutdown of the plant. In this paper, an autoregression (AR) model based on acoustic emission (AE) technique has been proposed to establish the qualitative relationship between AE signals and agglomeration in the HSBR. In this method, the frequency of AE signal varies with particles of different sizes striking the reactor walls. From the cold model experiments, it was found that AR power spectrum became fluctuant after the addition of agglomerations into laboratorial scale HSBR, and meanwhile the low frequency band energy ratio and the variance of AE signals kept rising. Furthermore, this AE-based AR model was also successfully applied to detect the agglomeration in an industrial HSBR unit, showing that the method could monitor agglomerations in an environmentally friendly manner and with fairly good accuracy. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26350328

Fault detection based on acoustic emission-early agglomeration recognition system in fluidized bed reactor / Yefeng Zhou in Industrial & engineering chemistry research, Vol. 50 N° 14 (Juillet 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 14 (Juillet 2011) . - pp. 8476-8484 Titre : Fault detection based on acoustic emission-early agglomeration recognition system in fluidized bed reactor Type de document : texte imprimé Auteurs : Yefeng Zhou, Auteur ; Kezeng Dong, Auteur ; Huang Zhengliang, Auteur Année de publication : 2011 Article en page(s) : pp. 8476-8484 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Fluidized  bed  reactor  Agglomeration  Acoustic  emission  Failure  detection Résumé : Agglomeration is one of the most challenging problems due to overheating of the particles in fluidized bed reactors (FBRs). Therefore, it is an urgent task to develop a reliable and sensitive method, which can help accurately detect the agglomeration in an early stage. In this study, acoustic emission-early agglomeration recognition system (AE-EARS) has been put forward for fault detection. Based on acoustic emission signals, the attractor comparison method was developed for prewarning the agglomeration in lab-scale and pilot-scale apparatus. The results concluded from this study demonstrated that the statistical characteristic S acts more sensitively to small agglomeration when compared with the malfunction coefficients CD2 and CK2, and other traditional measurement techniques (such as γ ray, temperature, and pressure difference). Besides, model optimization based on AE-EARS can help to select the criterion and improve the rate of false alarm. The analysis methods based on AE-EARS can warn the agglomeration earlier, faster, and more accurately. Especially the S value based on the attractor comparison, can be used as an indicator for "early recognition", which enjoys a broad prospect in industrial application. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24346887 [article] Fault detection based on acoustic emission-early agglomeration recognition system in fluidized bed reactor [texte imprimé] / Yefeng Zhou, Auteur ; Kezeng Dong, Auteur ; Huang Zhengliang, Auteur . - 2011 . - pp. 8476-8484. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 14 (Juillet 2011) . - pp. 8476-8484 Mots-clés : Fluidized  bed  reactor  Agglomeration  Acoustic  emission  Failure  detection Résumé : Agglomeration is one of the most challenging problems due to overheating of the particles in fluidized bed reactors (FBRs). Therefore, it is an urgent task to develop a reliable and sensitive method, which can help accurately detect the agglomeration in an early stage. In this study, acoustic emission-early agglomeration recognition system (AE-EARS) has been put forward for fault detection. Based on acoustic emission signals, the attractor comparison method was developed for prewarning the agglomeration in lab-scale and pilot-scale apparatus. The results concluded from this study demonstrated that the statistical characteristic S acts more sensitively to small agglomeration when compared with the malfunction coefficients CD2 and CK2, and other traditional measurement techniques (such as γ ray, temperature, and pressure difference). Besides, model optimization based on AE-EARS can help to select the criterion and improve the rate of false alarm. The analysis methods based on AE-EARS can warn the agglomeration earlier, faster, and more accurately. Especially the S value based on the attractor comparison, can be used as an indicator for "early recognition", which enjoys a broad prospect in industrial application. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24346887